One-against-all multicategory classification via discrete support vector machines
Carlotta Orsenigo, Carlo Vercellis · Virtual Community of Pathological Anatomy (University of Castilla La Mancha) · 2003
Discrete support vector machines (DSVM), recently proposed in [l01 and [l l] for binary classification problems, have been shown to outperform other competing approaches on well-known benchmark datasets. Here we address their extension to multicategory classification, by developing a one-against-all framework in which a set of binary discrimination problems are solved by means of DSVM. Computational tests on publicly available datasets are then conducted to compare multicategory DSVM with other methods, indicating that the proposed technique achieves high accuracies.